The relationship between resting metabolic rate and quality of life is moderated by age and body composition in women: a cross-sectional study

Health-related quality of life (HRQOL) is negatively influenced by chronic disease, with greater impairments noted as the number of disease diagnoses increases [1]. Furthermore, among those with chronic disease, gender differences exist, and women report significantly poorer HRQOL than men [2‐4]. This gender-specific vulnerbility has potentially severe long-term consequences for women because diminished HRQOL is associated with higher all cause mortality [5], mortality due to cancer and heart disease [6, 7], and depression and suicidal ideation [8]. Hence, there is an increasing focus on patient-related outcomes such as HRQOL to improve healthcare processes and outcomes [9‐11].

Body composition is also associated with quality of life [12]. Lean (muscle) mass positively influences quality of life [13, 14], while fat mass (obesity) exerts a negative influence [15, 16]. At the same time, body composition, specifically lean mass, positively influences resting metabolic rate (RMR) or the amount of calories burned at rest [17, 18], while a low RMR can negatively affect body composition by promoting increases in fat mass [19, 20] that diminish quality of life [15]. Unfortunately, gender differences are also observed for body composition, with women being at greater risk than men for negative changes that influence and are influenced by RMR (i.e. decreases in muscle and increases in fat) [21].

Resting metabolism can be improved by a variety of exercise and diet interventions that are not dependent on long-term changes in body composition. These include acute bouts of resistance or aerobic exercise [22‐24], dietary consumption of protein or essential fatty acids [25, 26], or an overall dietary pattern high in phytochemicals [27]. If a direct link between RMR and HRQOL can be established, this will open up a potentially wide range of interventions that may directly improve quality of life in women. However, we can find no study that has evaluated the direct relationship between RMR and HRQOL, although they are both linked to body composition. Therefore, the current study measured RMR, body composition, and HRQOL in order to identify direct and indirect relationships in women. Our primary hyposthesis was that RMR would be directly and positively related to HRQOL. Our secondary hypothesis regarding body composition was that lean mass would have a positive relationship with both RMR and HRQOL, while fat mass would have a negative relationship with both RMR and HRQOL.

One hundred twenty women between the ages of 25 – 89 years completed the study. Participant characteristics are reported in Table 1. The majority (82%) were white, while black and Hispanic women made up 17% of the sample. Sixty-three percent (n = 75) characterized themselves as either active or very active. Median waist circumference was 88 cm and median BMI was 26.1 kg/m², indicating borderline health risk [46]. Consistent with this risk, 27% reported hypertension and 18% reported a history of cancer. Although the median value for BMI was in the overweight category, actual values ranged from 17.2 kg/m² (underweight) to 51.9 kg/m² (severe obesity). Values for RMR ranged from 710–2160 kcal/day, with a median of 1165 kcal/day. Finally, median composite HRQOL scores of 84.0 (PCS) and 85.0 (MCS) reflected strongly positive perceptions of physical and mental quality of life.

Correlation analysis demonstrated no significant relationship directly between RMR and physical quality of life (PCS). However, RMR was directly and significantly related to mental quality of life (MCS), but the direction of association was negative (r = -0.282, p = 0.002). RMR was also significantly and positively related to body composition through absolute (kg) lean and fat mass (r = 0.709 and r = 0.339, p < 0.001, respectively), but only fat mass was significantly related to quality of life (PCS: r = -0.329, p < 0.001 and MCS: r = -0.319, p < 0.001), although the direction of the relationship was negative. Furthermore, RMR was strongly and positively related to LMI (r = 0.650, p < 0.001), but as with lean mass (kg), there was no relationship between LMI and either physical or mental quality of life. Last, RMR was significantly and positively related to BMI (r = 0.256, p = 0.005), which was in turn significantly and negatively related to both physical and mental quality of life (r = -0.366, p < 0.001 and r = -0.304, p < 0.001, respectively).

To further explain the absence of the expected direct and positive relationship between RMR and physical quality of life, we considered demographic variables. Age was found to have a significant and negative relationship to both RMR (r = -0.524, p < 0.001) and physical quality of life (r = -0.267, p = 0.003). However, it was not directly related to mental quality of life, or to BMI and fat mass. Explanatory models for HRQOL were developed based on the direction and strength of the relationships between variables (Figs. 1 and 2).

To confirm the moderating influence of age and body composition on the relationship between RMR and HRQOL, we entered them into multiple regression models sequentially based on the strength of the individual relationships. In total, RMR, age, fat mass, and BMI explained 24% (p < 0.001) of the variance in physical quality of life (Table 2). In total, RMR, fat mass, and BMI explained 15% (p < 0.001) of the variance in mental quality of life (Table 3).

To our knowledge this is the first study to evaluate the direct relationship between RMR and HRQOL in women. Although the predicted relationship was found with mental quality of life, it was not in the direction hypothesized. Furthermore, no direct relationship was observed with physical quality of life. Hence, our primary hypothesis was not upheld. Our secondary hypothesis was also not fully supported in so far as lean mass was only related to RMR and not to HRQOL. Furthermore, although fat mass was related to both RMR and HRQOL, the direction of the relationship was negative as we hypothesized only for HRQOL. Contrary to our hypothesis, fat mass was positively related to RMR, which we interpret to indicate that as women gain body mass (either fat or lean mass), RMR increases commensurately. This interpretation appears to be supported by the similar relationship observed between RMR and BMI, which is a normalized measure of body weight made up of lean and fat mass.

Our findings regarding the absence of a direct relationship between RMR and physical quality of life were unexpected. Logically, RMR would be expected to have a stronger relationship to the physical component of HRQOL than to the mental component. Physical changes involving loss of lean mass, strength, and function have a well-recognized negative effect on HRQOL [13, 14]. However, these changes are in fact age-related [47], as are decreases in RMR [48]. Although in the current study age was an unexpected moderator for physical HRQOL, Raczkiewicz and colleagues [49] recently reported that in very old women, increased age exerted a negative influence solely on the physical component of quality of life, which provides support for our findings. Further research in this area is needed in order to determine the extent to which age moderates the physical component of HRQOL.

Although the reason why our hypotheses were not supported is unclear, it seems likely that we did not fully appreciate the implications of age. Decreased RMR has been observed with increased age in both women and men across the lifespan [50]. Furthermore, this decrease can exceed what would be predicted based on age-related changes in lean and fat mass, and has not been found to be influenced by participation in physical activity or exercise [51]. In clinical populations such as women with breast cancer, age does not appear to be directly related to HRQOL [52], and instead HRQOL may either increase or decrease with age [53]. Functional ability has been found to be a strong driver of HRQOL in these women [53], and function decreases as age increases [54]. Notably in the current study, age specifically moderated the relationship between RMR and the physical component of HRQOL. Somewhat surprisingly, we can find no study reporting normal changes in HRQOL in healthy women across the lifespan, and instead research seems to have focused specifically on clinical populations [55‐57]. Kroenke and colleague [5] previously alluded to this gap in the evidence but subsequent work has not followed. Given the negative and intervening relationship we observed, normal age-related changes in HRQOL should be clarified.

There are other implications of age as a moderator of physical HRQOL. Age may provide a novel strategy by which clinicians can target interventions that increase RMR and potentially improve HRQOL in women. Brief exercise training bouts can produce transient increases in resting metabolism in women in their fifth and sixth decades [22], while dietary consumption of omega-3 fatty acids increases resting metabolism in women in their seventh and eighth decades [26]. Based on the current findings and in recognition of the moderating influence of age, interventions such as these could be directed specifically at older women in order to increase efficiency and clinical effectiveness.

Body composition was also found to moderate the relationship between RMR and both the physical and mental components of HRQOL. Unexpectedly though, only fat mass was found to exert an effect, and although lean mass was strongly related to RMR, the relationship did not extend to HRQOL. Also unexpectedly, fat mass had a positive rather than a negative relationship with RMR, which is contrary to earlier research by Buscemi and colleagues [20]. We anticipated that because a low metabolic rate has been associated with obesity and weight gain [20, 58, 59], women with lower RMR in the current study would demonstrate greater fat mass and likely greater BMI. Mechanistically, a lower RMR could contribute to weight gain since it could be easier to overconsume and push the pathway to kcal storage (i.e., gains in fat mass). Our findings contradicted that, and instead greater accrual of fat mass and body weight (normalized as BMI) was associated with increases in RMR. Body weight or size has been positively associated with increased RMR, but primarily through lean mass [60]. It seems possible that fat mass may also have an effect, especially in the presence of higher body weight manifested as higher BMI. Since fat mass is metabolically active [61], as women gain body fat their RMR may increase since they are adding metabolically active tissue. This would be of particular importance to elucidate given the negative direction of the relationship we observed between body composition and HRQOL.

In women, the relationship between RMR and HRQOL is moderated by age, fat mass, and BMI. Although RMR has a direct relationship with mental quality of life, the intervening effect of fat mass and BMI is somewhat stronger. By comparison, RMR has no direct effect on physical quality of life and instead, age is negatively related to both. In addition, fat mass and BMI have an intervening influence that is similar to their relationship with mental quality of life. Although these moderating factors were unexpected, understanding the primary and secondary pathways leading to HRQOL in women will allow clinicians and researchers to direct interventions more effectively by targeting specific variables along those pathways, thereby improving health outcomes.